The burden of cardiovascular diseases linked to different risk factors in the context of lifestyle patterns: the ATTICA study (2002-2022)

Abstract Background/Introduction The stagnant cost of cardiovascular disease (CVD) (i.e., approximately €282 billion annually) could be diminished by managing well-known modifiable risk factors, including socio-demographic, clinical, lifestyle and psychological factors. Albeit research on these factors and their impact on CVD is robust, research on the interactions of such factors is limited. Purpose To investigate variations in the burden of CVD associated with different modifiable risk factors in the context of lifestyle patterns. Methods The sample was 1,988 (mean age: 45±14 years, 49.7% male) individuals from the ATTICA cohort study (2002-2022), who were initially free-of-CVD. Throughout follow-up assessments (conducted in 2002, 2006, 2012, and 2022), trained health professionals evaluated combined fatal/non-fatal CVD outcomes, using WHO-ICD-10 criteria. Six categories of modifiable risk factors were assessed: 1. low/middle socio-economic status (SES) vs. high, 2. urban area of residence vs. rural, 3. body mass index (BMI), 4. clinical factors (diabetes mellitus, hypertension, hypercholesterolemia, chronic kidney disease), 5. lifestyle factors (smoking, physical inactivity, unhealthy dietary habits) and, 6. psychological factors (anxiety, depression). Population attributable fractions (PAF) were computed for each sole factor, as well as different combinations of these factors, representing different lifestyle patterns (please see Table). Results The 20-year CVD incidence was 36% (n=718). A lifestyle pattern comprising of having all 6 categories of CVD risk factors was associated with a PAF of 82%; 8 out of 10 CVD cases would have been prevented if all these factors had been completely managed in the sample (Table). For lifestyle patterns comprising of all 6 categories, except for one, PAF varied between 69% and 80%, when participants had all 5 factors, but were healthy (with no clinical factors) or of high SES, respectively (Table). For lifestyle patterns comprising of only 2 categories, PAF varied between 25% when participants had low/middle SES and an urban residence, and 59%, when participants had at least 1 clinical factor and an increased BMI (Table). Please see the Table for more risk factor combinations. Conclusion The study findings are important because they offer a more individualized approach to treat an individual, shedding light on which CVD risk factors should be modified first, based on the rest of the factors affecting the individual, and their combined burden on CVD risk.Table